Applying Design for Six Sigma to Software and Hardware Systems (paperback)

by Maass, Eric; McNair, Patricia D.

• ISBN13: 9780133359466
• ISBN10: 0133359468
• Edition: 1st
• Format: Paperback
• Copyright: 2009-08-19
• Publisher: Prentice Hall

Summary

The Practical, Example-Rich Guide to Building Better Systems, Software, and Hardware with DFSS Design for Six Sigma (DFSS) offers engineers powerful opportunities to develop more successful systems, software, hardware, and processes. In Applying Design for Six Sigma to Software and Hardware Systems, two leading experts offer a realistic, step-by-step process for succeeding with DFSS. Their clear, start-to-finish roadmap is designed for successfully developing complex high-technology products and systems that require both software and hardware development. Drawing on their unsurpassed experience leading Six Sigma at Motorola, the authors cover the entire project lifecycle, from business case through scheduling, customer-driven requirements gathering through execution. They provide real-world examples for applying their techniques to software alone, hardware alone, and systems composed of both. Product developers will find proven job aids and specific guidance about what teams and team members need to do at every stage. Using this book’s integrated, systems approach, marketers, software professionals, and hardware developers can converge all their efforts on what really matters: addressing the customer’s true needs. Learn how to Ensure that your entire team shares a solid understanding of customer needs Define measurable critical parameters that reflect customer requirements Thoroughly assess business case risk and opportunity in the context of product roadmaps and portfolios Prioritize development decisions and scheduling in the face of resource constraints Flow critical parameters down to quantifiable, verifiable requirements for every sub-process, subsystem, and component Use predictive engineering and advanced optimization to build products that robustly handle variations in manufacturing and usage Verify system capabilities and reliability based on pilots or early production samples Master new statistical techniques for ensuring that supply chains deliver on time, with minimal inventory Choose the right DFSS tools, using the authors’ step-by-step flowchart If you’re an engineer involved in developing any new technology solution, this book will help you reflect the real Voice of the Customer, achieve better results faster, and eliminate fingerpointing. About the Web Site The accompanying Web site,sigmaexperts.com/dfss, provides an interactive DFSS flowchart, templates, exercises, examples, and tools.

Author Biography

Eric Maass has thirty years of experience with Motorola, ranging from research and development through manufacturing, to director of operations for a $160 million business and director of design and systems engineering for Motorola’s RF Products Division. Dr.Maass was a cofounder of the Six Sigma methods at Motorola, and was a key advocate for the focus on variance reduction; his article on a “Strategy to Reduce Variance” was published in 1987, the year that Motorola announced Six Sigma. He codeveloped a patented method for multiple response optimization that has resulted in over 60 first-pass successful new products, and most recently has been the lead Master Black Belt for Design for Six Sigma at Motorola. He coauthored the Handbook of Fiber Optic Data Communication and a variety of chapters in books and articles ranging from concept selection to augmentation of design of experiments to multiple response optimization to advanced decision-making methods. Dr. Maass’s other accomplishments include driving the turnaround of the Logic Division from “virtual chapter 11” to second-most profitable division (of 22 divisions) in two years, and he also won the contract for Freescale Semiconductor’s largest customer, Qualcomm. Dr. Maass has a rather diverse educational background, with a B.A. in biological sciences, an M.S. in chemical and biomedical engineering, a Ph.D. in industrial engineering, and nearly thirty years’ experience in electrical engineering. Dr. Maass is currently consulting with and advising several companies and institutions including Motorola, Arizona State University, Oracle, and Eaton.

Patricia McNair is the director of Motorola’s software Design for Six Sigma program and a Certified Six Sigma Master Black Belt. She served as cochair of the Software Development Consortium and program director of the Motorola Six Sigma Software Academy. She travels internationally to various countries including France, England, China, Singapore, India, Malaysia, Brazil, and many others for consulting and training of Motorola engineers.
    She spent more than twenty-five years in software and systems engineering roles including systems engineering manager, design engineer manager, architect and requirements lead, senior process manager, certified SEI instructor for the introduction to CMMI, certified Six Sigma black belt, and authorized SEI CBA IPI lead assessor for various companies such as Motorola, GE Healthcare, and IBM Federal Systems, where she worked through and managed all phases of a software development life cycle, from requirement gathering, design, development, and implementation, to production and support.
    She has served as an adjunct professor at De Paul University in Chicago, the State University of New York at Binghamton, and at the University of Phoenix.
    She holds an M.S. in computer science from the State University of New York at Binghamton and an MBA from the Lake Forest Graduate School of Management.

Table of Contents

Foreword      xvii
Preface      xxi
Acknowledgments      xxvii
About the Authors      xxix

Chapter 1: Introduction: History and Overview of DFSS      1
A Brief Historical Perspective on Six Sigma and Design for Six Sigma (DFSS) 1
Historical Perspective on Design for Six Sigma 8
DFSS Example 14
Summary 27

Chapter 2: DFSS Deployment      29
Ideal Scenario for DFSS Deployment 29
Steps Involved in a Successful DFSS Deployment 30
DFSS Deployment: Single Project 45
Minimum Set of Tools, and the “One Tool Syndrome” 47
Goals for DFSS 48
“The DFSS Project was a Success, But . . .” 50
Summary 50

Chapter 3: Governance, Success Metrics, Risks, and Certification      53
DFSS Governance 53
Success Metrics 57
Product Development Risks 58
DFSS Certification 62
Summary 64

Chapter 4: Overview of DFSS Phases      65
DFSS for Projects, Including Software and Hardware 65
DFSS Process Nomenclatures 69
Requirements Phase 73
Architecture Phase 75
Architecture Phase for the Software Aspects 78
Design Phase 78
Integrate Phase 78
Optimize Phase 78
Verify Phase 80
Summary 82

Chapter 5: Portfolio Decision Making and Business Case Risk      83
Position within DFSS Flow 83
Portfolio Decision Making as an Optimization Process 84
Financial Metric 85
Portfolio Decisions and Resource Constraints 89
Goals, Constraints, Considerations, and Distractions 91
Adjusting Portfolio Decisions Based on Existing Commitments and the Organization’s Strategic Direction 92
Summary: Addressing Business Case Risk 94

Chapter 6: Project Schedule Risk      95
Position within DFSS Flow 95
Project Schedule Model 95
The “Fuzzy Front End” and Delays Caused by Changing Requirements 97
Time for First Pass: Critical Path versus Critical Chain 98
Critical Chain/Theory of Constraints Project Management Behaviors 103
Iterations, Qualification, and Release to Product 105
Summary: Addressing Schedule Risk 106

Chapter 7: Gathering Voice of the Customer to Prioritize Technical Requirements      107
Importance and Position within DFSS Flow 107
VOC Purpose and Objectives 110
The VOC Gathering (Interviewing) Team 110
Customer Selection 111
Voices and Images 112
Customer Interview Guide 113
Planning Customer Visits and Interviews 115
Customer Interviews 116
KJ Analysis: Grouping, Structuring and Filtering the VOC 117
Identifying Challenging Customer Requirements (NUDs) 120
Kano Analysis 122
Validation and Prioritization of Customer Requirements 124
Translating Customer Requirements to System Requirements: The System-Level House of Quality 124
Constructing a House of Quality 128
Summary: VOC Gathering—Tying It All Together 134

Chapter 8: Concept Generation and Selection      137
Position within DFSS Flow 137
Concept Generation Approaches 137
Brainstorming and Mind-Mapping 140
TRIZ 141
Alternative Architecture Generation: Hardware and Software 143
Generation of Robust Design Concepts 146
Consideration of Existing Solutions 147
Feasibility Screening 148
Developing Feasible Concepts to Consistent Levels 148
Concept Selection 149
Summary 152
Appendix: Kansei Engineering 152

Chapter 9: Identification of Critical Parameters and FMEA      153
Position within DFSS Flow 153
Definition of a Critical Parameter 153
Considerations from VOB and Constraints 155
Prioritization and Selection of Critical Parameters 157
FMEA 160
Software FMEA Process (Software Systems, Software Subsystems, and Software Components FMEA) 164
Software FMEA Implementation Case Study 169
Considerations of Reliability and Availability 172
Examples of Critical Parameters 174
Summary 176
Appendix: Software FMEA Process Documentation 176

Chapter 10: Requirements Flow-Down      187
Position within DFSS Flow 187
Flow-Down for Hardware and Software Systems 190
Anticipation of Potential Problems: P-Diagrams and DFMEA 193
Target and Spec Limits 197
Measurement System Analysis 198
Capability Analysis 202
Flow-Down or Decomposition 203
Flow-Down Examples 206
Initial Tolerance Allocation 208
Summary 210

Chapter 11: Software DFSS and Agile      211
Measuring the Agile Design 218
Summary 221

Chapter 12: Software Architecture Decisions      223
Software Architecture Decision-Making Process 224
Using Design Heuristics to Make Decisions 227
Using Architecture Tactics to Make Decisions 228
Using DFSS Design Trade-Off Analysis to Make Decisions 230
Using Design Patterns, Simulation, Modeling, and Prototyping for Decisions 234
Summary 235

Chapter 13: Predictive Engineering: Continuous and Discrete Transfer Functions      237
Discrete versus Continuous Critical Parameters 238
Methods for Deriving a Transfer Function for a Discrete Critical Parameter 241
Logistic Regression for Discrete Parameters 242
Methods for Deriving a Transfer Function for a Continuous or Ordinal Critical Parameter 244
Existing or Derived Equation (First Principles Modeling) 245
Modeling within a Spreadsheet, Mathematical Modeling Software, or Simulation Software 246
Empirical Modeling using Historical Data: Regression
Analysis and General Linear Model 247
Empirical Modeling using Design of Experiments 251
Empirical Modeling using Response Surface Methods 256
DOE with Simulators: Design and Analysis of Computer Experiments (DACE) 259
Summary 261

Chapter 14: Predictive Engineering: Optimization and Critical Parameter Flow-Up      263
Critical Parameter Flow-Up: Monte Carlo Simulation 266
Critical Parameter Flow-Up: Generation of System Moments (Root Sum of Squares) 267
Critical Parameter Scorecard 269
Selecting Critical Parameters for Optimization 270
Optimization: Mean and/or Variance 271
Optimization: Robustness through Variance Reduction 273
Multiple Response Optimization 280
Cooptimization of Cpk’s 282
Yield Surface Modeling 283
Case Study: Integrated Alternator Regulator (IAR) IC for Automotive 288
Summary 290

Chapter 15: Predictive Engineering: Software Optimization      293
Multiple Response Optimization in Software 293
Use Case Modeling in Optimization 294
Evaluate the Model 298
Software Mistake Proofing 299
Software Stability 303
Summary 305

Chapter 16: Verification of Design Capability: Hardware      307
Position within DFSS Flow 307
Measurement System Analysis (MSA) 307
Improvements for Inadequate Measurement Systems 310
The Risk of Failures Despite Verification: Test Escapes 313
Determine the Capability 315
Summary 316

Chapter 17: Verification of Reliability and Availability      319
Customer Perspective 319
Availability and Reliability Flow Down 321
Bathtub Curve and Weibull Model 322
Software Reliability 325
Early Life Failures/Infant Mortality 326
Useful Life/Constant Failure Rate 326
Wear Out 327
Detailed Flowchart for Reliability Optimization and Verification 327
Accelerated Life Testing 328
WeiBayes: Zero Failures Obtained from ALT 330
Risk of Failures Despite Verification: Reliability Test Escapes 331
Methods to Improve Reliability and Availability 332
Summary 333
Appendix: Case Studies—Software Reliability, and System Availability (Hardware and Software Availability) 333

Chapter 18: Verification: Software Testing Combined with DFSS Techniques      347
Software Verification Test Strategy Using Six Sigma 350
Controlling Software Test Case Development through Design Patterns 354
Improving Software Verification Testing Using Combinatorial Design Methods 356
Summary 358
Bibliography 359
Glossary of Common Software Testing Terms 359

Chapter 19: Verification of Supply Chain Readiness      363
Position within DFSS Flow 363
Verification that Tolerance Expectations Will Be Met 366
Confidence in Robust Product Assembly (DFMA) 366
Verification of Appropriate and Acceptable Interface Flows 369
Confidence in the Product Launch Schedule 369
Confidence in Meeting On-Time Delivery and Lead-Time Commitments 370
Case Study: Optoelectronic Multichip Module 380
Summary 382

Chapter 20: Summary and Future Directions      385
Future Directions 386

Index       391